Structure learning in graphical models incorporating the scale-free prior
Xiao Guo, Jiang-Lun Wu, Hai Zhang · Scientia Sinica Informationis · 2016
In this paper, we consider the problem of structure learning in graphical models under the prior that the underlying networks are scale free. We propose a novel regularization model, which incorporates the scale-free prior, with a penalty that is a hybrid of the Log-type and $L_q$-type penalty functions. An iterative reweighted $L_1$ algorithm is employed to solve the model. Numerical studies show that our method is both effective and practical and performs well in terms of parameter estimation and model selection.